Spectral Image Data Fusion for Multisource Data Augmentation

• URL: http://arxiv.org/abs/2405.14883v1
• Date: Fri, 5 Apr 2024 13:40:18 GMT
• Title: Spectral Image Data Fusion for Multisource Data Augmentation
• Authors: Roberta Iuliana Luca, Alexandra Baicoianu, Ioana Cristina Plajer,
• Abstract summary: Multispectral and hyperspectral images are increasingly popular in different research fields, such as remote sensing, astronomical imaging, or precision agriculture. The amount of free data available to perform machine learning tasks is relatively small. Artificial intelligence models developed in the area of spectral imaging require input images with a fixed spectral signature.
• Score: 44.99833362998488
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multispectral and hyperspectral images are increasingly popular in different research fields, such as remote sensing, astronomical imaging, or precision agriculture. However, the amount of free data available to perform machine learning tasks is relatively small. Moreover, artificial intelligence models developed in the area of spectral imaging require input images with a fixed spectral signature, expecting the data to have the same number of spectral bands or the same spectral resolution. This requirement significantly reduces the number of usable sources that can be used for a given model. The scope of this study is to introduce a methodology for spectral image data fusion, in order to allow machine learning models to be trained and/or used on data from a larger number of sources, thus providing better generalization. For this purpose, we propose different interpolation techniques, in order to make multisource spectral data compatible with each other. The interpolation outcomes are evaluated through various approaches. This includes direct assessments using surface plots and metrics such as a Custom Mean Squared Error (CMSE) and the Normalized Difference Vegetation Index (NDVI). Additionally, indirect evaluation is done by estimating their impact on machine learning model training, particularly for semantic segmentation.

Related papers

• Analysis of Classifier Training on Synthetic Data for Cross-Domain Datasets [4.696575161583618]
  This study focuses on camera-based traffic sign recognition applications for advanced driver assistance systems and autonomous driving. The proposed augmentation pipeline of synthetic datasets includes novel augmentation processes such as structured shadows and gaussian specular highlights. Experiments showed that a synthetic image-based approach outperforms in most cases real image-based training when applied to cross-domain test datasets.
  arXiv  Detail & Related papers  (2024-10-30T07:11:41Z)
• Hyperspectral Dataset and Deep Learning methods for Waste from Electric and Electronic Equipment Identification (WEEE) [0.0]
  We evaluate the performance of diverse deep learning architectures for hyperspectral image segmentation. Results show that incorporating spatial information alongside spectral data leads to improved segmentation results. We contribute to the field by cleaning and publicly releasing the Tecnalia WEEE Hyperspectral dataset.
  arXiv  Detail & Related papers  (2024-07-05T13:45:11Z)
• SatSynth: Augmenting Image-Mask Pairs through Diffusion Models for Aerial Semantic Segmentation [69.42764583465508]
  We explore the potential of generative image diffusion to address the scarcity of annotated data in earth observation tasks. To the best of our knowledge, we are the first to generate both images and corresponding masks for satellite segmentation.
  arXiv  Detail & Related papers  (2024-03-25T10:30:22Z)
• SpectralGPT: Spectral Remote Sensing Foundation Model [60.023956954916414]
  A universal RS foundation model, named SpectralGPT, is purpose-built to handle spectral RS images using a novel 3D generative pretrained transformer (GPT) Compared to existing foundation models, SpectralGPT accommodates input images with varying sizes, resolutions, time series, and regions in a progressive training fashion, enabling full utilization of extensive RS big data. Our evaluation highlights significant performance improvements with pretrained SpectralGPT models, signifying substantial potential in advancing spectral RS big data applications within the field of geoscience.
  arXiv  Detail & Related papers  (2023-11-13T07:09:30Z)
• StableLLaVA: Enhanced Visual Instruction Tuning with Synthesized Image-Dialogue Data [129.92449761766025]
  We propose a novel data collection methodology that synchronously synthesizes images and dialogues for visual instruction tuning. This approach harnesses the power of generative models, marrying the abilities of ChatGPT and text-to-image generative models. Our research includes comprehensive experiments conducted on various datasets.
  arXiv  Detail & Related papers  (2023-08-20T12:43:52Z)
• Probabilistic Deep Metric Learning for Hyperspectral Image Classification [91.5747859691553]
  This paper proposes a probabilistic deep metric learning framework for hyperspectral image classification. It aims to predict the category of each pixel for an image captured by hyperspectral sensors. Our framework can be readily applied to existing hyperspectral image classification methods.
  arXiv  Detail & Related papers  (2022-11-15T17:57:12Z)
• Alternative Data Augmentation for Industrial Monitoring using Adversarial Learning [0.0]
  This study examines an industry application of data synthesization using generative adversarial networks. We apply two different methods to create binary labels: a problem-tailored trigonometric function and a WGAN model. The labels are translated into color images using pix2pix and used to train a U-Net.
  arXiv  Detail & Related papers  (2022-05-09T12:16:38Z)
• Learn to Ignore: Domain Adaptation for Multi-Site MRI Analysis [1.3079444139643956]
  We present a novel method that learns to ignore the scanner-related features present in the images, while learning features relevant for the classification task. Our method outperforms state-of-the-art domain adaptation methods on a classification task between Multiple Sclerosis patients and healthy subjects.
  arXiv  Detail & Related papers  (2021-10-13T15:40:50Z)
• VAE-Info-cGAN: Generating Synthetic Images by Combining Pixel-level and Feature-level Geospatial Conditional Inputs [0.0]
  We present a conditional generative model for synthesizing semantically rich images simultaneously conditioned on a pixellevel (PLC) and a featurelevel condition (FLC) Experiments on a GPS dataset show that the proposed model can accurately generate various forms of macroscopic aggregates across different geographic locations.
  arXiv  Detail & Related papers  (2020-12-08T03:46:19Z)
• Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation [84.7571086566595]
  We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape. The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
  arXiv  Detail & Related papers  (2020-03-31T11:50:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.